• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.669-677
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• 2020

As the number of Internet users exploded, attacks on the web increased. In addition, the attack patterns have been diversified to bypass existing defense techniques. Traditional web firewalls are difficult to detect attacks of unknown patterns.Therefore, the method of detecting abnormal behavior by artificial intelligence has been studied as an alternative. Specifically, attempts have been made to apply natural language processing techniques because the type of script or query being exploited consists of text. However, because there are many unknown words in scripts and queries, natural language processing requires a different approach. In this paper, we propose a new classification model which uses byte pair encoding (BPE) technology to learn the embedding vector, that is often used for web attack payloads, and uses an attention mechanism-based Bi-GRU neural network to extract a set of tokens that learn their order and importance. For major web attacks such as SQL injection, cross-site scripting, and command injection attacks, the accuracy of the proposed classification method is about 0.9990 and its accuracy outperforms the model suggested in the previous study.

• Geomechanics and Engineering
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• v.12 no.4
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• pp.723-738
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• 2017

Rock is a heterogeneous material, which introduces complexity in the analysis of rock slopes, since both the existing discontinuities within the rock mass and the intact rock contribute to the degradation of strength. Rock failure is often catastrophic due to the brittle nature of the material, involving the sliding along structural planes and the fracturing of rock bridge. This paper proposes an advanced discretization method of rock mass based on block theory. An in-house software, GeoSMA-3D, has been developed to generate the discrete fracture network (DFN) model, considering both measured and artificial joints. Measured joints are obtained from the photogrammetry analysis on the excavation face. Statistical tools then facilitate to derive artificial joints within the rock mass. Key blocks are searched to provide guidance on potential reinforcement measures. The discretized blocky system is subsequently implemented into a discontinuous deformation analysis (DDA) code. Strength reduction technique is employed to analyze the stability of the slope, where the factor of safety can be obtained once excessive deformation of slope profile is observed. The combined analysis approach also provides the failure mode, which can be used to guide the choice of strengthening strategy if needed. Finally, an illustrated example is presented for the analysis of a rock slope of 20 m height inclined at $60^{\circ}$ using combined GeoSMA-3D and DDA calculation.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.86-96
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• 2020

Based on the use cases of image recognition technology, this study looked at how artificial intelligence plays a role in image recognition technology. Through image recognition technology, satellite images can be analyzed with artificial intelligence to reveal the calculation of oil storage tanks in certain countries. And image recognition technology makes it possible for searching images or products similar to images taken or downloaded by users, as well as arranging fruit yields, or detecting plant diseases. Based on deep learning and neural network algorithms, we can recognize people's age, gender, and mood, confirming that image recognition technology is being applied in various industries. In this study, we can look at the use cases of domestic and overseas image recognition technology, as well as see which methods are being applied to the industry. In addition, through this study, the direction of future research was presented, focusing on various successful cases in which image recognition technology was implemented and applied in various industries. At the conclusion, it can be considered that the direction in which domestic image recognition technology should move forward in the future.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.643-650
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• 2017

Photovoltaic generation which has unlimited energy sources are very intermittent because they depend on the weather. Therefore, it is necessary to get accurate generation prediction with reducing the uncertainty of photovoltaic generation and improvement of the economics. The Meteorological Agency predicts weather factors for three days, but doesn't predict the sunshine and solar radiation that are most correlated with the prediction of photovoltaic generation. In this study, we predict sunshine and solar radiation using weather, precipitation, wind direction, wind speed, humidity, and cloudiness which is forecasted for three days at Meteorological Agency. The photovoltaic generation forecasting model is proposed by using predicted solar radiation and sunshine. As a result, the proposed model showed better results in the error rate indexes such as MAE, RMSE, and MAPE than the model that predicts photovoltaic generation without radiation and sunshine. In addition, DNN showed a lower error rate index than using SVM, which is a type of machine learning.

• Tunnel and Underground Space
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• v.24 no.3
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• pp.234-242
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• 2014

Deep drilling project should be managed systematically and efficiently because it is significantly influenced by various related factors having uncertainty and high risk in terms of economy and effective management. In particular, drilling project involves participants from various sectors including necessary service company and it also needs their collaboration by sharing related information occurring at drilling process in order to secure efficient performance management. We developed 4D (3D + time) information based visualization system for progress management by combining 3D design model and predicted optimized control parameters for each section in geothermal well design. We also applied PDM (precedence diagramming method) to the system in order to setup the effective process model and hooked it up to 3D information based on precedence relation and required time for informatized process network.

• Journal of the Korean Wood Science and Technology
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• v.45 no.6
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• pp.797-808
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• 2017

Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.49.2-49.2
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• 2016

The number of discovery of asteroids with peculiar rotational states has recently increased, and hence a novel approach for lightcurve analysis is considered to be critical. In order to investigate objects such as Non-Principal Axis (NPA) rotator, we selected a NPA candidate, (5247) Kryolv as our target considering its Principal Axis Rotation (PAR) code and the visibility in early 2016. The observations of Krylov were made using Korea Microlensing Telescope Network (KMTNet) 1.6 m telescopes installed at the three southern sites with TO (Target of Opportunity) observation mode. We conducted R-band time-series photometry over a total of 51 nights from January to April 2016 with several exposures during each allocated run. The ensemble normalization photometry was employed using the AAVSO Photomtric All-Sky Survey (APASS) catalog for the standardization. We successfully confirmed its NPA spin state based on the deviation from the reduced lightcurve, and thus Krylov is recorded as the first NPA rotator of its kind in the main-belt, with its precession and rotation periods, $P{\varphi}=81.18h$ and $P_{\Psi}=67.17h$, respectively. In this paper, we present the spin direction, the 3D shape model and taxonomy of the newly confirmed NPA asteroid (5247) Krylov.

• Journal of the Microelectronics and Packaging Society
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• v.12 no.3 s.36
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• pp.267-274
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• 2005

This paper presents the fabrication of surface acoustic wave (SAW)-based pressure sensor for long-term stable mechanical compression force measurement. SAW pressure sensor has many attractive features for practical pressure measurement: no battery requirement, wireless pressure detection especially at hazardous environments, and easy other functionality integrations such as temperature, humidity, and RFID. A $41^{\circ}$ YX $LiNbO_3$ piezoelectric substrate was used because of its high SAW propagation velocity and large values of electromechanical coupling factors $K^2$. A silicon substrate with $\~200{\mu}m$ deep cavity was bonded to the diaphragm with epoxy, in which gold was covered all over the inner cavity in order to confine electromagnetic energy inside the sensor, and provide good isolation of the device from its environment. The reflection coefficient $S_{11}$ was measured using network analyzer. High S/N ratio, sharp reflected peaks, and clear separation between the peaks were observed. As a mechanical compression force was applied to the diaphragm from top with extremely sharp object, the diaphragm was bended, resulting in the phase shifts of the reflected peaks. The phase shifts were modulated depending on the amount of applied mechanical compression force. The measured $S_{11}$ results showed a good agreement with simulated results obtained from equivalent admittance circuit modeling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3168-3186
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• 2020

Automatic topic essay generation (TEG) is a controllable text generation task that aims to generate informative, diverse, and topic-consistent essays based on multiple topics. To make the generated essays of high quality, a reasonable method should consider both diversity and topic-consistency. Another essential issue is the intrinsic link of the topics, which contributes to making the essays closely surround the semantics of provided topics. However, it remains challenging for TEG to fill the semantic gap between source topic words and target output, and a more powerful model is needed to capture the semantics of given topics. To this end, we propose a pretraining-based contextual self-attention (PC-SAN) model that is built upon the seq2seq framework. For the encoder of our model, we employ a dynamic weight sum of layers from BERT to fully utilize the semantics of topics, which is of great help to fill the gap and improve the quality of the generated essays. In the decoding phase, we also transform the target-side contextual history information into the query layers to alleviate the lack of context in typical self-attention networks (SANs). Experimental results on large-scale paragraph-level Chinese corpora verify that our model is capable of generating diverse, topic-consistent text and essentially makes improvements as compare to strong baselines. Furthermore, extensive analysis validates the effectiveness of contextual embeddings from BERT and contextual history information in SANs.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.19-27
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• 2020

In this study, Developing high resolution camera and Social Network Service sharing image can be easily getting images, it cause about taking fingerprints to easy from images. So I present solution about prevent to taking fingerprints. this technology is develop python using to opencv, blur libraries. First of all 'Hand Key point Detection' algorithm is used to locate the hand in the image. Using this algorithm can be find finger joints that can be protected while minimizing damage in the original image by using the coordinates of separate blurring the area of fingerprints in the image. from now on the development of accurate finger tracking algorithms, fingerprints will be protected by using technology as an internal option for smartphone camera apps from high resolution images.